What We Do
When a new EHR system gets selected, the focus of data governance teams quickly shifts to the topic of medical data conversion.
And, those who have been through an application-to-application clinical data transition know that discrete EHR conversion can come with challenging data mapping issues. The difficulty lies in transforming and loading clinical information from a source system to a destination system when the destination system represents formatted source system data in a different context. That’s why EMR data conversion services are so specialized and critical. You only get one chance to ensure the integrity of your clinical data when it migrates from one EHR to another, so, it must be right. Lives depend on it.
Harmony Healthcare IT, an industry leader in data management, has been ranked as the top data extraction and migration healthcare IT company according Black Book™ Rankings, a division of Black Book™ Market Research. For over a decade, we have specialized in both discrete and non-discrete EMR and ERP data conversion for healthcare delivery organizations. Our U.S.-based team of EMR conversion specialists have extracted and converted clinical, financial and business data from hundreds of ambulatory, acute, and ancillary EHR and ERP software brands, such as Epic and Oracle Cerner. We tackle the complexity and variability of the EHR and ERP conversion process with our systematic approach, detailed planning, and decades of clinical data conversion experience.
Our enhanced data conversion offering, through a strategic relationship with DrFirst, employs artificial intelligence (AI) and machine learning (ML) to automate the process of migrating structured data from one EHR to another to inform clinical decision-making. Learn more.
In scoping and planning the data conversion, we typically start with the destination EHR go-live date and work backward, determining the timing of initial, subsequent and differential data pulls. We scope medical conversions record requirements and gain a solid understanding of the destination system data ingestion specifications. We establish clear goals, determine testing and validation plans and set expectations around the timing, effort and resource allocation it will take to hit deadlines and budget allocations.
Access is gained to the source system so that data can be extracted and imported into a more common structure for analysis and modeling. In cases where application vendors host the application, data is obtained so that data transformation can take place.
The purpose here is to determine the accuracy of the source system data, identifying potential issues, inconsistencies or duplications. During this process, we define the source data, understand its relationships, its usage, and how it’s structured compared to the destination database schema. Based on feedback from end users and stakeholders, we prepare the data to be transformed and loaded into the destination system. We address differences in data structure from the source to the destination system. Data is then modeled to fit the destination specifications for data ingestion.
Through testing, we identify issues, understand which data can be migrated and which cannot, and determine how long the migration will take (load and run times). In validation, a field-by-field comparison of the source to the destination system is conducted – from a representative sampling of records – to ensure the integrity of the data is complete and accurate.
Data is delivered in the format specified by the receiving vendor to ingest. Typical file formats in which discrete data is delivered are CCDA/CCD, CSV, XML, HL7, Flat File. FHIR is also an option.
Learn more about our enhanced way of completing data migrations, through artificial intelligence and machine learning.
A roadmap for healthcare providers engaged in determining and implementing best practices for managing legacy data.
This webinar co-presented with DrFirst covers data conversion strategies that will reduce manual data reconciliation effort through the use of AI and benefit clinicians upon go-live of an EHR transition.